Equity

Quantitative Trading - Part 4

概要

Analysts developing data models face a number of key data challenges, including biases – such as confirmation and availability biases – bad data, and model inaccuracies. One key type of data model, known as machine learning, allows the user to query the model for answers to simple questions. This eCourse provides an overview of major pitfalls in developing data models and discusses the importance of ML in detail.

宗旨

On completion of this course, you will be able to:
- Recognise the importance of alternative data, including big data and expert data
- Recall how biases, bad data, and model inaccuracies can all affect the handling of data
- Identify the key features of both supervised and unsupervised machine learning (ML)
- Recognise how dimension reduction reduces the dimension of a data set and how data clustering groups large amounts of multi-dimensional data

內容

Quantitative Trading – Data & Machine Learning
Topic 1: Overview
Topic 2: Alternative Data
Topic 3: Big Data & Expert Data
Topic 4: Biases
Topic 5: Machine Learning (ML)
Topic 6: Supervised & Unsupervised ML
Topic 7: Dimension Reduction
Topic 8: Data Clustering

詳情

活動編號
TEPEQ21004201
地點
網上平台
相關主題
第1類 - 證券交易,第9類 - 提供資產管理
語言
英文
課程時數
SFC:1.00, PWMA:1.00
費用
所有會員: HKD305
非會員: HKD445
機構會員員工: HKD305